New to Spark Scala, I just want to read a json file and post the content to an external rest api server. Can anyone provide a simple example? or provide guidelines?
You probably do not want to use Spark for this. Spark is an analytical engine for processing large amounts of data - unless you're reading in massive amounts of json from hdfs, this task is more suitable for scala. You should look up ways to read a json file in scala, and send that content to a server in scala.
Here are some great places to get started:
Scala Read JSON file
https://alvinalexander.com/scala/how-to-send-json-post-data-to-restful-url-in-scala
The following is from the above URL:
import java.io._
import org.apache.commons._
import org.apache.http._
import org.apache.http.client._
import org.apache.http.client.methods.HttpPost
import org.apache.http.impl.client.DefaultHttpClient
import java.util.ArrayList
import org.apache.http.message.BasicNameValuePair
import org.apache.http.client.entity.UrlEncodedFormEntity
import com.google.gson.Gson
case class Person(firstName: String, lastName: String, age: Int)
object HttpJsonPostTest extends App {
// create our object as a json string
val spock = new Person("Leonard", "Nimoy", 82)
val spockAsJson = new Gson().toJson(spock)
// add name value pairs to a post object
val post = new HttpPost("http://localhost:8080/posttest")
val nameValuePairs = new ArrayList[NameValuePair]()
nameValuePairs.add(new BasicNameValuePair("JSON", spockAsJson))
post.setEntity(new UrlEncodedFormEntity(nameValuePairs))
// send the post request
val client = new DefaultHttpClient
val response = client.execute(post)
println("--- HEADERS ---")
response.getAllHeaders.foreach(arg => println(arg))
}
Related
Im using below scala code to receive messages from rabbitmq.
import com.rabbitmq.client.Channel
import com.rabbitmq.client.Connection
import com.rabbitmq.client.ConnectionFactory
import com.rabbitmq.client.ConsumerCancelledException
import com.rabbitmq.client.QueueingConsumer
val rabbitMQconnection = getRabbitMQConnection
val channel = rabbitMQconnection.createChannel()
val args = Map[String, AnyRef]("x-message-ttl" -> Long.box(40000))
channel.queueDeclare("test",true,false,false,args )
val consumer = new QueueingConsumer(channel)
channel.basicConsume("test",true,consumer)
var message: String = null
val delivery = consumer.nextDelivery()
message= new String(delivery.getBody(), StandardCharsets.UTF_8)
println("at consumer : " +message)
My input data from RabbitMq is zipped Json data. When I use the above code, Im unable to unzip the data. Could someone please let me know how to read zipped json data.
Thank you.
I have another question about Spark and Scala. I want to use that technologie to get data and generate a xml.
Therefore, I want to know if it is possible to create node ourself (not automatic creation) and what library can we use ? I search but I found nothing very interesting(Like I'm new in this technologie, I don't know many keywords).
I want to know if there is in Spark something like this code (I write that in scala. It works in local but I can't use new File() in Spark).
val docBuilder: DocumentBuilder = DocumentBuilderFactory.newInstance().newDocumentBuilder()
val document = docBuilder.newDocument()
ar root:Element = document.createElement("<name Balise>")
attr = document.createAttribute("<attr1>")
attr.setValue("<value attr1>")
root.setAttributeNode(<attr>)
attr = document.createAttribute("<attr2>")
attr.setValue("<value attr2>")
root.setAttributeNode(attr)
document.appendChild(root)
document.setXmlStandalone(true)
var transformerFactory:TransformerFactory = TransformerFactory.newInstance()
var transformer:Transformer = transformerFactory.newTransformer()
var domSource:DOMSource = new DOMSource(document)
var streamResult:StreamResult = new StreamResult(new File(destination))
transformer.transform(domSource,streamResult)
I want to know if it's possible to do that with spark.
Thanks for your answer and have a good day.
Not exactly, but you can do something similar by using Spark XML API pr XStream API on Spark.
First try using Spark XML API which is most useful when reading and writing XML files using Spark. However, At the time of writing this, Spark XML has following limitations.
1) Adding attribute to root element has not supported.
2) Does not support following structure where you have header and footer elements.
<parent>
<header></header>
<dataset>
<data attr="1"> suports xml tags and data here</data>
<data attr="2">value2</data>
</dataset>
<footer></footer>
</parent>
If you have one root element and following data then Spark XML is go to api.
Alternatively, you can look at XStream API. Below are steps how to use it to create custom XML structures.
1) First, create a Scala class similar to the structure you wanted in XML.
case class XMLData(name:String, value:String, attr:String)
2) Create an instance of this class
val data = XMLData("bookName","AnyValue", "AttributeValue")
3) Conver data object to XML using XStream API. If you already have data in a DataFrame, then do a map transformation to convert data to an XML string and store it back in DataFrame. if you do so, then you can skip step #4
val xstream = new XStream(new DomDriver)
val xmlString = xstream.toXML(data)
4) Now convert xmlString to DataFrame
val df = xmlString.toDF()
5) Finally, write to a file
df.write.text("file://filename")
Here isa full sample example with XStream API
import com.thoughtworks.xstream.XStream
import com.thoughtworks.xstream.io.xml.DomDriver
import org.apache.spark.sql.SparkSession
case class Animal(cri:String,taille:Int)
object SparkXMLUsingXStream{
def main(args: Array[String]): Unit = {
val spark = SparkSession.
builder.master ("local[*]")
.appName ("sparkbyexamples.com")
.getOrCreate ()
var animal:Animal = Animal("Rugissement",150)
val xstream1 = new XStream(new DomDriver())
xstream1.alias("testAni",classOf[Animal])
xstream1.aliasField("cricri",classOf[Animal],"cri")
val xmlString = Seq(xstream1.toXML(animal))
import spark.implicits._
val newDf = xmlString.toDF()
newDf.show(false)
}
}
Hope this helps !!
Thanks
I am fairly new to spray, and I would like to extract the result returned by this API to a list variable.
What would be the best way to achieve this?
If we ignore error handling, you could do it like so:
import scala.io.Source
import spray.json._
import DefaultJsonProtocol._
...
val source = Source.fromURL("https://api.guildwars2.com/v2/items")
val json = source.mkString.parseJson
val list = json.convertTo[List[Int]]
Event after lots of try and googling, could not get the fileName, if I am use the streaming context. I can use the wholeTextFiles of SparkContext but, then I have to re-implement the streaming context's functionality.
Note: FileName (error events as json file) is the input to the system, so retaining the name in the output is extremely important so that any event can be traced during audit.
Note: FileName is of the format below. SerialNumber part can be extracted from the event json, but time is stored as milliseconds and difficult to get in below format in a reliable way and no way to find the counter.
...
Each file contains just one line as a complex json string. Using the streaming context I am able to create a RDD[String], where each string is a json string from a single file. Can any one have any solution/workaround for associating the strings with the respective file name.
val sc = new SparkContext("local[*]", "test")
val ssc = new StreamingContext(sc, Seconds(4))
val dStream = ssc.textFileStream(pathOfDirToStream)
dStream.foreachRDD { eventsRdd => /* How to get the file name */ }
You could do this using fileStream and creating your own FileInputFormat, similar to TextInputFormat which uses the InputSplit to provide the filename as a Key. Then you can use fileStream to get a DStream with filename and line.
Hi to get file names from DStream I have created a java function which fetch file path using java spark api and than in spark-streaming(which is written in scala) i have called that function.
Here is a java Code sample:
import java.io.Serializable;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.rdd.NewHadoopPartition;
import org.apache.spark.rdd.UnionPartition;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.Partition;
public class GetFileNameFromStream implements Serializable{
public String getFileName(Partition partition)
{
UnionPartition upp = (UnionPartition)partition;
NewHadoopPartition npp = (NewHadoopPartition) upp.parentPartition();
String filePath=npp.serializableHadoopSplit().value().toString();
return filePath;
}
}
In spark streaming, i have called above java function
Here is a code sample
val obj =new GetFileNameFromStream
dstream.transform(rdd=>{
val lenPartition = rdd.partitions.length
val listPartitions = rdd.partitions
for(part <-listPartitions){
var filePath=obj.getFileName(part)
})
I'm trying to compile my first scala program and I'm using twitterStream to get tweets, here is a snippet of my code:
import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.twitter._
import org.apache.spark.streaming.StreamingContext._
import TutorialHelper._
object Tutorial {
def main(args: Array[String]) {
// Location of the Spark directory
val sparkHome = "/home/shaza90/spark-1.1.0"
// URL of the Spark cluster
val sparkUrl = TutorialHelper.getSparkUrl()
// Location of the required JAR files
val jarFile = "target/scala-2.10/tutorial_2.10-0.1-SNAPSHOT.jar"
// HDFS directory for checkpointing
val checkpointDir = TutorialHelper.getHdfsUrl() + "/checkpoint/"
// Configure Twitter credentials using twitter.txt
TutorialHelper.configureTwitterCredentials()
val ssc = new StreamingContext(sparkUrl, "Tutorial", Seconds(1), sparkHome, Seq(jarFile))
val tweets = ssc.twitterStream()
val statuses = tweets.map(status => status.getText())
statuses.print()
ssc.checkpoint(checkpointDir)
ssc.start()
}
}
When compiling I'm getting this error message:
value twitterStream is not a member of org.apache.spark.streaming.StreamingContext
Do you know if I'm missing any library or dependency?
In this case you want a stream of tweets. We all know that Sparks provides Streams. Now, lets check if Spark itself provides something for interacting with twitter specifically.
Open Spark API-docs -> http://spark.apache.org/docs/1.2.0/api/scala/index.html#package
Now search for twitter and bingo... there is something called TwitterUtils in package org.apache.spark.streaming. Now since it is called TwitterUtils and is in package org.apache.spark.streaming, I think it will provide helpers to create stream from twitter API's.
Now lets click on TwitterUtils and goto -> http://spark.apache.org/docs/1.2.0/api/scala/index.html#org.apache.spark.streaming.dstream.ReceiverInputDStream
And yup... it has a method with following signature
def createStream(
ssc: StreamingContext,
twitterAuth: Option[Authorization],
filters: Seq[String] = Nil,
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): ReceiverInputDStream[Status]
It returns a ReceiverInputDStream[ Status ] where Status is twitter4j.Status.
Parameters are further explained
ssc
StreamingContext object
twitterAuth
Twitter4J authentication, or None to use Twitter4J's default OAuth authorization; this uses the system properties twitter4j.oauth.consumerKey, twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and twitter4j.oauth.accessTokenSecret
filters
Set of filter strings to get only those tweets that match them
storageLevel
Storage level to use for storing the received objects
See... API docs are simple. I believe, now you should be a little more motivated to read API docs.
And... This means you need to look a little( at least getting started part ) into twitter4j documentation too.
NOTE :: This answer is specifically written to explain "Why not to shy
away from API docs ?". And was written after careful thoughts. So
please, do not edit unless your edit makes some significant
contribution.